Facing Big Data
Facing Big Data: methods and skills needed for a 21st century sociology
Funding: NRP 75 “Big Data” , Swiss National Science Foundation (SNSF)
Duration: January 1, 2017 - June 30, 2020
The rise of Big Data - data that are large, diverse, often unstructured, and concern an array of phenomena - poses new challenges as well as opportunities to the social sciences and their claim to empirically analyze and explain the social world. While commercial and administrative use of Big Data is currently creating new professions of “data scientists” and “data journalists”, sociologists are much slower in joining research using Big Data in their analyses. Yet sociologists are primed to participate in Big Data analyses because of their training linking theoretical concepts with empirical observations about the social world. This peculiar disjuncture is the starting point of this project. The project contends that Big Data makes changes in methodological training necessary. In order to cope with the pending “crisis of empirical sociology”, sociological methods training needs to become part of the “digital enlightenment” necessary to analyze and interpret such types of Big Data. The goal of the project then is to empirically examine the current state of methods and tools used in training and in practice in three data analytic fields, in order to map the current state of the fields, to demonstrate how knowledge domains are maintained and changed, and to provide recommendations for practitioners and relevant policy-makers for expanded tool kits.
Theoretically, the project approaches these questions on fields of knowledge and their emergence from perspectives of the sociology of science and organizational sociology. Methodologically, it combines traditional social science methods with computational methods, using interviews, field notes, as well as large textual corpora to map and analyze the professional and organizational fields using network analyses. The project is divided into four subprojects, with interspersed workshops for training and international exchange. Building on an interdisciplinary, especially Anglo-US literature, the project sets out to empirically assess the current state of methods training in German speaking sociology and, in parallel moves, investigate the emerging fields of data science and data journalism with respect to their required methodological tool kits. Based on a comparative analysis of those three fields that study social behavior, the project will identify core strengths as well as notable divergences in order to provide recommendations for strengthening existing methods repertoires and to expand training. The subprojects on methods training in German speaking sociology and the emerging fields of data science and data journalism are doctoral student projects, while the PI is responsible for the synthesizing subproject.
Overall, the project contributes to the social science research capacities in Switzerland and their international competitiveness to understand and deal with the methodological and, in turn, educational challenges facing Big Data. Expanded competencies in methods will enable sociology graduates to actively engage in the field of Big Data driven academic, commercial and administrative research, thereby contributing to their employability. Data fluency together with classical sociological skills can give rise to new opportunities for brokerage of subject and technical expertise across disciplinary boundaries, in the academe but also in many commercial and administrative workplaces, wherever people are needed to interpret social data. But the project will not only aim to provide recommendations for the extension of sociological tool kits. It will also deliver recommendations for the other two data analytic fields. Moreover, as a quest for data literacy, data fluency, data curation and analytic skills spreads across the disciplines, this project’s recommendations can also yield indications for training in other social sciences and humanities.
The project in a minute: